When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use...

Rare or narrowly distributed species may be threatened by stressors to which they have never been exposed or for which data are very limited. In such cases the species response cannot be predicted on the basis of directly measured data, but may be inferred from the response of one or more appropriate surrogate species. Here, I propose a practical way to use the stressor response of one or more ...

Background Nowadays, numerical simulations constitute a common tool in science and engineering activities. It is especially used for prediction and decision making, or simply for a better understanding of physical phenomena. However, in order to give an accurate representation of the real world, a large set of parameters and nonlinearities may need to be introduced in the mathematical models in...

ion in this study area. Different groundwater extraction scenarios were generated using Latin hypercube sampling. The salinity concentrations resulting from each of these pumping patterns are simulated using FEMWATER. The simulated salinity level and the corresponding pumping rates form the input-output pattern. Altogether 230 extraction patterns are used in this study. Different realizations o...

The cryogenic cavitation has critical implications on the performance and safety of liquid rocket engines. In this study, a systematic investigation based on the surrogate modeling techniques is conducted to assess and improve the performance of a transport-based cryogenic cavitation model. Based on the surrogate model, global sensitivity analysis is be conducted to assess the role of model par...

Relational database systems are attracting interest from users outside the commercial areas for which such systems were initially designed. This paper describes a surrogate model for engineering data (CAD, VLSI, geographical data, etc.) which allows the user to define structural relationships among semantically related data. A binding mechanism between system-controlled surrogates and user-defi...

Current models to measure protein turnover across muscle bed are based on many surrogate measures of amino acyl-tRNA. We measured muscle protein turnover based on tracer-to-tracee ratios of the stable isotopes of leucine, phenylalanine, and ketoisocaproate (KIC) in artery and vein and muscle amino acyl-tRNA and muscle tissue fluid (TF) in 26 healthy subjects. A three-compartment model calculati...

We investigate surrogate-assisted strategies for derivative-free optimization using the mesh adaptive direct search (MADS) blackbox optimization algorithm. In particular, we build an ensemble of surrogate models to be used within the search step of MADS, and examine different methods for selecting the best model for a given problem at hand. To do so, we introduce an order-based error tailored t...

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional dist...